I almost finished my data science bootcamp with Flatiron School. It was a challenging time full of new information and tasks. We were focused on mathematics, computer science and domain expertise This time for a new challenge - job search. I decided to start my own preparation tips in the blog.
**Interview Preparation Grid **
Go through each of the projects or components of your resume and ensure that you can talk about them in detail. I think grid like this may helps:
| Common Question | Project 1 | Project 2 |
|---|---|---|
| Technical tags | ||
| Challenges | ||
| Mistake/Failures | ||
| Enjoyed | ||
| Leadership | ||
| Conflicts | ||
| What You’d Do Differently |
Reducing each story to just a couple of keywords may make the grid easier to remember. This step helps me to focus my attention on the main part of questions about projects:
- know your technical project
- be specific
- limit details
- focus on yourself in team projects
- give structure answers
Be specific means - giving just the fact and letting the interviewer derive an interpretation. For example, rather than saying that I “create all interesting models”, I can instead describe the specific steps I did that were challenging.
Stay light on details and just state the key points. When possible, tru to translate it or at least explain the impact. This offers the interviewers the opportunity to drill in further.
Structuring response to a question means you should follow pattern: Situation,action, Result.
Technical Questions
There can be a lot of variants of interview topics for data science.They can start from simple methodics and coding tasks and finish after “Take home project”, where the company will test your coding, analytical, and communication skills at the same time.
Listen.
Listen carefully. Pay very close attention to any information in the task description. You probably need it all for the correct answer. Ask questions about anything you’re unsure about.
Coding question
If it is a coding question then your should walking through a problem:
- Ask about example
- Brute force solution as soon as possible
- Optimize your solution
- Walk through your approach in detail
- Implement
- Test your code
If you could draw an example - do it!
Sometimes your path to the answer can be illustrated. For example, the data science process workflow can be represented by a block diagram.